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Tingfeng Xu,1,* Liying Ren,1,* Minjun Liao,1,2,* Bigeng Zhao,1 Rongyu Wei,1 Zhipeng Zhou,3 Yong He,4 Hao Zhang,3 Dongbo Chen,5 Hongsong Chen,5 Weijia Liao1 1Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin, 541001, Guangxi, People’s Republic of China; 2Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, People’s Republic of China; 3Department of Radiology, Affiliated Hospital of Guilin Medical University, Guilin, 541001, Guangxi, People’s Republic of China; 4Department of Radiology, the Second Affiliated Hospital of Guilin Medical University, Guilin, 541001, Guangxi, People’s Republic of China; 5Peking University People’s Hospital, Peking University Hepatology Institute, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Disease, Beijing, 100044, People’s Republic of China*These authors contributed equally to this workCorrespondence: Weijia Liao, Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin, 541001, Guangxi, People’s Republic of China, Tel +86 773 2833021, Fax +86 773 2822703, Email liaoweijia288@163.com Hongsong Chen, Peking University People’s Hospital, Peking University Hepatology Institute, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Disease, No. 11 Xizhimen South Street, Beijing, 100044, People’s Republic of China, Tel +86 10 88325724, Email chenhongsong2999@163.comPurpose: Microvascular invasion (MVI) impairs long-term prognosis of patients with hepatocellular carcinoma (HCC). We aimed to develop a novel nomogram to predict MVI and patients’ prognosis based on radiomic features of contrast-enhanced CT (CECT).Patients and Methods: HCC patients who underwent curative resection were enrolled. The radiomic features were extracted from the region of tumor, and the optimal MVI-related radiomic features were selected and applied to construct radiomic signature (Rad-score). The prediction models were created according to the logistic regression and evaluated. Biomarkers were analyzed via q-PCR from randomly selected HCC patients. Correlations between biomarkers and radiomic signature were analyzed.Results: A total of 421 HCC patients were enrolled. A total of 1962 radiomic features were extracted from the region of tumor, and the 11 optimal MVI-related radiomic features showed a favor predictive ability with area under the curves (AUCs) of 0.796 and 0.810 in training and validation cohorts, respectively. Aspartate aminotransferase (AST), tumor number, alpha-fetoprotein (AFP) level, and radiomics signature were independent risk factors of MVI. The four factors were integrated into the novel nomogram, named as CRM, with AUCs of 0.767 in training cohort and 0.793 in validation cohort for predicting MVI, best among radiomics signature alone and clinical model. The nomogram was well-calibrated with favorable clinical value demonstrated by decision curve analysis and can divide patients into high- or low-risk subgroups of recurrence and mortality. In addition, gene BCAT1, DTGCU2, DOCK3 were analyzed via q-PCR and serum AFP were identified as having significant association with radiomics signature.Conclusion: The novel nomogram demonstrated good performance in preoperatively predicting the probability of MVI, which might guide clinical decision.Keywords: hepatocellular carcinoma, microvascular invasion, preoperative noninvasive diagnosis, contrast-enhanced CT, biologic correlation

Background Since it’s a challenging task to precisely predict the prognosis of patients with hepatocellular carcinoma (HCC). We developed a nomogram based on a novel indicator GMWG [(Geometric Mean of gamma-glutamyltranspeptidase (GGT) and white blood cell (WBC)] and explored its potential in the prognosis for HCC patients. Methods The patients enrolled in this study were randomly assigned to training and validation cohorts. And we performed the Least Absolute Shrinkage and Selection Operator proportional hazards model (LASSO Cox) model with clinical characteristics, serum indexes, and novel GMWG. Multivariate analysis was performed to build a nomogram. The performance of the nomogram was evaluated by C-index, the area under the receiver operating characteristic curve (AUC), and the calibration curve. Kaplan-Meier curves showed discrimination of the nomogram. Clinical utility was assessed by decision curve analysis (DCA). The discrimination ability of the nomogram was determined by the net reclassification index (NRI). Results The geometric mean of GGT and white WBC count (GMWG), neutrophil to lymphocyte ratio (NLR), and tumor size were significantly associated with the overall survival (OS). The variables above were used to develop the nomogram. The indexes of nomogram were 0.70 and 071 in the training or validation cohort, respectively. AUC of 1-, 3- and 5-year OS showed satisfactory accuracy as well. The calibration curve showed agreement between the ideal and predicted values. Kaplan-Meier curves based on the overall survival (OS) and disease-free survival (DFS) showed significant differences between nomogram predictive low and high groups. DCA showed clinical utilities while NRI showed discrimination ability in both training or validation cohort. Conclusions GMWG might be a potential prognostic indicator for patients with HCC. The nomogram containing GMWG also showed satisfaction prediction capacity.